Early Exiting with Ensemble Internal Classifiers.
Tianxiang SunYunhua ZhouXiangyang LiuXinyu ZhangHao JiangZhao CaoXuanjing HuangXipeng QiuPublished in: CoRR (2021)
Keyphrases
- ensemble learning
- ensemble classifier
- classifier ensemble
- ensemble pruning
- multiple classifiers
- training data
- majority voting
- training set
- final classification
- ensemble methods
- combining classifiers
- imbalanced data
- individual classifiers
- multiple classifier systems
- decision tree classifiers
- mining concept drifting data streams
- feature selection
- weak learners
- class label noise
- ensemble members
- trained classifiers
- randomized trees
- decision trees
- weighted voting
- majority vote
- diversity measures
- pruning method
- concept drifting data streams
- neural network
- feature ranking
- machine learning algorithms
- learning algorithm
- support vector
- rule induction algorithm
- random forests
- one class support vector machines
- accurate classifiers
- linear classifiers
- training examples
- ensemble classification
- classification algorithm
- machine learning methods
- weak classifiers
- linear support vector machines
- generalization ability
- base classifiers
- classification models
- test set
- class labels
- prediction accuracy
- publicly available data sets
- logistic regression
- pruning algorithm
- feature subset
- classifier combination
- naive bayes
- bias variance decomposition
- training samples
- classification systems
- base learners